Amazon SageMaker è un servizio gestito per sviluppatori e data scientist che consente di progettare, addestrare e distribuire modelli di Machine Learning su larga scala. In questo webinar esploreremo le funzionalità di questo servizio, dalle istanze notebook Jupyter ai servizi di training e hosting, per poi discutere di aspetti come il labeling di dataset e l’ottimizzazione dei modelli. Successivamente, vedremo in modo pratico come utilizzare il servizio per implementare, addestrare e distribuire un modello di esempio.
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Work with Machine Learning in Amazon SageMaker - BDA203 - Toronto AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Learning Objectives:
- Learn how Amazon SageMaker can be used for exploratory data analysis before training
- Learn how Amazon SageMaker provides managed distributed training with flexibility
- Learn how easy it is to deploy your models for hosting within Amazon SageMaker
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Work with Machine Learning in Amazon SageMaker - BDA203 - Toronto AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Learning Objectives:
- Learn how Amazon SageMaker can be used for exploratory data analysis before training
- Learn how Amazon SageMaker provides managed distributed training with flexibility
- Learn how easy it is to deploy your models for hosting within Amazon SageMaker
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
In this session, we discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.
Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ...Amazon Web Services
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples.
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
Predicting the Future with Amazon SageMaker
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this session you will learn how to use built-in, high performance machine learning algorithms for predictions and computer vision within your application. We will deploy machine learning models into production and start generating classifications with a few API calls using the SageMaker SDK. Additionally we will demonstrate how to run your custom trained machine learning model directly out of your web application to classify incoming user generated content.
Steve Shirkey, ASEAN Solutions Architect, Amazon Web Services
Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS...Amazon Web Services
Financial institutions want to accelerate and scale their use of machine learning (ML), but going from a hypothesis to a working ML model that infers answers in production requires much time and effort. Continuous integration and deployment techniques can help by accelerating the ML development process while providing a way to answer questions about data lineage, such as, “What version of the code and data produced this particular inference?” In this session, learn how to combine Amazon SageMaker with AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline to create a workflow that helps provide the reproducibility and auditability that financial institutions need without constraining the tools and methods that data scientists use to build their ML models.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Following simple patterns of good application design can allow you to scale your application for your customers easily. This presentation dives into the 12 factor application design and demo how this applies to containers and deployments on Amazon ECS and Fargate. We'll take a look at tooling that can be used to simplify your workflow and help you adopt the principles of the 12 factor application.
Workshop slides for the introduction to Amazon SageMaker, and integration of Amazon SageMaker with other tools within your AWS environment. Visit https://aws.amazon.com/sagemaker for more information.
Move Your Desktops and Applications to AWS with Amazon WorkSpaces and AppStre...Amazon Web Services
IT organizations today need to support a modern, flexible, global workforce and ensure that their users can be productive anywhere. Moving desktops and applications to the AWS Cloud offers improved security, scale, and performance with cloud economics. In this session, we provide an overview of Amazon WorkSpaces and Amazon AppStream 2.0, and we discuss the use cases for each. Then, we dive deep into best practices for implementing Amazon WorkSpaces and AppStream 2.0, including how to integrate with your existing identity, security, networking, and storage solutions.
Introduction to AI services for Developers - Builders Day IsraelAmazon Web Services
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Machine Learning with Amazon SageMaker - Algorithms and Frameworks - BDA304 -...Amazon Web Services
Algorithms and frameworks form a fundamental part of machine learning (ML). These critical components enable developers and data scientists to easily and quickly build ML models with well-defined interfaces for a range of use cases. The most commonly used algorithms and frameworks, built-in with Amazon SageMaker, make ML easier to address these use cases. In this session, we discuss the built-in algorithms and frameworks and how you can leverage them for your ML models. We also discuss the flexibility of bringing your own algorithm into Amazon SageMaker depending on your needs.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
In this session, we discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.
Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ...Amazon Web Services
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples.
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
Predicting the Future with Amazon SageMaker
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this session you will learn how to use built-in, high performance machine learning algorithms for predictions and computer vision within your application. We will deploy machine learning models into production and start generating classifications with a few API calls using the SageMaker SDK. Additionally we will demonstrate how to run your custom trained machine learning model directly out of your web application to classify incoming user generated content.
Steve Shirkey, ASEAN Solutions Architect, Amazon Web Services
Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS...Amazon Web Services
Financial institutions want to accelerate and scale their use of machine learning (ML), but going from a hypothesis to a working ML model that infers answers in production requires much time and effort. Continuous integration and deployment techniques can help by accelerating the ML development process while providing a way to answer questions about data lineage, such as, “What version of the code and data produced this particular inference?” In this session, learn how to combine Amazon SageMaker with AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline to create a workflow that helps provide the reproducibility and auditability that financial institutions need without constraining the tools and methods that data scientists use to build their ML models.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Following simple patterns of good application design can allow you to scale your application for your customers easily. This presentation dives into the 12 factor application design and demo how this applies to containers and deployments on Amazon ECS and Fargate. We'll take a look at tooling that can be used to simplify your workflow and help you adopt the principles of the 12 factor application.
Workshop slides for the introduction to Amazon SageMaker, and integration of Amazon SageMaker with other tools within your AWS environment. Visit https://aws.amazon.com/sagemaker for more information.
Move Your Desktops and Applications to AWS with Amazon WorkSpaces and AppStre...Amazon Web Services
IT organizations today need to support a modern, flexible, global workforce and ensure that their users can be productive anywhere. Moving desktops and applications to the AWS Cloud offers improved security, scale, and performance with cloud economics. In this session, we provide an overview of Amazon WorkSpaces and Amazon AppStream 2.0, and we discuss the use cases for each. Then, we dive deep into best practices for implementing Amazon WorkSpaces and AppStream 2.0, including how to integrate with your existing identity, security, networking, and storage solutions.
Introduction to AI services for Developers - Builders Day IsraelAmazon Web Services
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Machine Learning with Amazon SageMaker - Algorithms and Frameworks - BDA304 -...Amazon Web Services
Algorithms and frameworks form a fundamental part of machine learning (ML). These critical components enable developers and data scientists to easily and quickly build ML models with well-defined interfaces for a range of use cases. The most commonly used algorithms and frameworks, built-in with Amazon SageMaker, make ML easier to address these use cases. In this session, we discuss the built-in algorithms and frameworks and how you can leverage them for your ML models. We also discuss the flexibility of bringing your own algorithm into Amazon SageMaker depending on your needs.
Accelerate Machine Learning with Ease Using Amazon SageMaker - BDA301 - Chica...Amazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we discuss and dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019Amazon Web Services
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Applying Maching Learning to Build Smarter Video WorkflowsAmazon Web Services
Christopher Kuthan, Worldwide Business Development Lead, Media - Solutions, AWS
This session provided a deep-dive into how you can harness the capabilities of Machine Learning to build smarter video workflows, create additional content value, and transform the viewing experience. This session incorporated live demonstrations of video use cases.
Accelerate Machine Learning with Ease using Amazon SageMakerAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we introduce Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
Learn how to get started with Amazon SageMaker—our fully-managed service that spans the entire machine learning (ML) workflow—so you can build, train, and deploy models quickly. Use Amazon SageMaker to label and prepare your data, choose an algorithm, train, tune, and optimize it for deployment, make predictions, and take action. Get your models to production faster with Amazon SageMaker SDKs, builder tools, and APIs tailored to your programming language or platform. Also, discover how Amazon SageMaker Ground Truth can aid in the adoption of ML technology for your organization.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes those challenges. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
Artificial Intelligence (AI) services on the AWS cloud bring the experience of Amazon and power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the history of AI at Amazon and explore the opportunities to apply one or more of the AI services provide a number of examples and use cases to help you get started.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
1. AWS Online Tech Talks
La serie di webinar AWS in Italiano
Amazon SageMaker in action: modelli di Machine Learning da idee a produzione
Speaker
Giuseppe Porcelli
Solutions Architect, AWS
Obiettivi
Fornire una panoramica del servizio Amazon SageMaker
Fornire linee guida su sviluppo, training e distribuzione di modelli di
Machine Learning
Mostrare in modo pratico come iniziare ad utilizzare Amazon SageMaker